Summary

The individual in this role will be responsible for interpreting quantitative data and developing statistical models to forecast and monitor infrastructure demand for iCloud and other Apple services.

Key Qualifications

2+ years of experience doing complex forecasting and data analysis

Strong statistical background and experience with time series modeling (e.g. ARIMA, exponential smoothing, time series regression methods etc.)

Experienced R programmer also proficient in other languages important to the ETL data pipeline (e.g. SQL)

Experience with data visualization packages (e.g. ggplot2, plotly

Excellent collaborator with strong written and verbal communication skills

Comfortable working in a loosely structured organization and advancing multiple projects at once on a tight schedule

Ability to share results with a non technical audience

Experience building and maintaining R packages

Innate curiosity

Experience with bayesian time series modeling and Stan (or Stan interfaces e.g. brms, rstanarm, rstan) is a plus but not required

Experience building interactive dashboards and apps (e.g. Shiny) is a plus but not required

A passion for statistics, forecasting, and R is also a plus but also not required

Description

As members of the Services Forecast & Efficiency data science team, we work with various engineering teams to understand current and future infrastructure demand (storage, network, CPU, etc.). We need to be persistent and flexible in extracting data from various sources, cleaning and curating these data, and then clearly and concisely summarizing any insights.
In this role you will build models to forecast the financial impact of new hardware and software releases across different scenarios and develop internal visualization and modeling tools to facilitate data-driven decisions. You should be comfortable communicating results and other analytical findings to business partners.

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